tests/testthat/test-multitypePoisson.R

library("testthat")

context("test-multitypePoisson.R")

#
# Small Poisson MLE regression
#

test_that("Small multi-type Poisson dense regression", {
    dobson1 <- data.frame(
        counts = c(18,17,15,20,10,20,25,13,12),
        outcome = gl(3,1,9),
        treatment = gl(3,3)
    )
    dobson <- rbind(dobson1, dobson1)
    dobson$type = as.factor(c(rep("A",9),rep("B",9)))
    tolerance <- 1E-4

    goldFit <- glm(counts ~ outcome + treatment, data = dobson1, family = poisson())

    # glmFit <- glm(counts ~ outcome + treatment, data = dobson, contrasts = dobson$type,
    #               family = poisson()) # gold standard

    dataPtrD <- createCyclopsData(counts ~ outcome + treatment, data = dobson,
                                       type = dobson$type,
                                       modelType = "pr", method = "debug")

    cyclopsFitD <- fitCyclopsModel(dataPtrD,
                           prior = createPrior("none"),
                           control = createControl(noiseLevel = "silent"))

    dataPtrE <- createCyclopsData(Multitype(counts, type) ~ outcome + treatment, data = dobson,
                                       modelType = "pr", method = "debug")

    cyclopsFitE <- fitCyclopsModel(dataPtrE,
                                   prior = createPrior("none"),
                                   control = createControl(noiseLevel = "silent"))

    expect_equal(coef(cyclopsFitD), coef(cyclopsFitE))

    dataPtrI <- createCyclopsData(Multitype(counts, type) ~ 1, indicatorFormula = ~ outcome + treatment, data = dobson,
                                       modelType = "pr", method = "debug")

    cyclopsFitI <- fitCyclopsModel(dataPtrI,
                                   prior = createPrior("none"),
                                   control = createControl(noiseLevel = "silent"))

    expect_equal(coef(cyclopsFitI), coef(cyclopsFitD))

    dataPtrS <- createCyclopsData(Multitype(counts, type) ~ 1, sparseFormula = ~ outcome + treatment, data = dobson,
                                       modelType = "pr", method = "debug")

    cyclopsFitS <- fitCyclopsModel(dataPtrS,
                                   prior = createPrior("none"),
                                   control = createControl(noiseLevel = "silent"))

    expect_equal(coef(cyclopsFitS), coef(cyclopsFitD))
})

test_that("coef throws error when not converged", {
    dobson1 <- data.frame(
        counts = c(18,17,15,20,10,20,25,13,12),
        outcome = gl(3,1,9),
        treatment = gl(3,3)
    )
    dobson2 <- data.frame(
        counts = c(18,17,15,20,10,20,25,13,12)-10,
        outcome = gl(3,1,9),
        treatment = gl(3,3)
    )
    dobson <- rbind(dobson1, dobson2)
    dobson$type = as.factor(c(rep("A",9),rep("B",9)))
    tolerance <- 1E-4

    dataPtrD <- createCyclopsData(Multitype(counts, type) ~ outcome + treatment, data = dobson,
                                       modelType = "pr")

    cyclopsFitD <- fitCyclopsModel(dataPtrD,
                                   prior = createPrior(c("normal","normal"), c(0.0001,10), graph = "type"),
                                   control = createControl(noiseLevel = "silent"))
    expect_error(coef(cyclopsFitD), "did not converge")
})


test_that("confirm dimension check", {
    dobson1 <- data.frame(
        counts = c(18,17,15,20,10,20,25,13,12),
        outcome = gl(3,1,9),
        treatment = gl(3,3)
    )
    dobson2 <- data.frame(
        counts = c(18,17,15,20,10,20,25,13,12)-10,
        outcome = gl(3,1,9),
        treatment = gl(3,3)
    )
    dobson <- rbind(dobson1, dobson2)
    dobson$type = as.factor(c(rep("A",9),rep("B",9)))
    tolerance <- 1E-4

    dataPtrD <- createCyclopsData(Multitype(counts, type) ~ outcome + treatment, data = dobson,
                                       modelType = "pr")


    expect_error(fitCyclopsModel(dataPtrD,
                                 prior = createPrior(c("normal"), c(0.0001,10), graph = "type"),
                                 control = createControl(noiseLevel = "silent")), "dimensionality mismatch")
    expect_error(fitCyclopsModel(dataPtrD,
                                 prior = createPrior(c("normal", "normal"), c(0.0001), graph = "type"),
                                 control = createControl(noiseLevel = "silent")), "dimensionality mismatch")
})


test_that("Small multi-type Poisson with hierarchical prior", {
    dobson1 <- data.frame(
        counts = c(18,17,15,20,10,20,25,13,12),
        outcome = gl(3,1,9),
        treatment = gl(3,3)
    )
    dobson2 <- data.frame(
        counts = c(18,17,15,20,10,20,25,13,12)-10,
        outcome = gl(3,1,9),
        treatment = gl(3,3)
    )
    dobson <- rbind(dobson1, dobson2)
    dobson$type = as.factor(c(rep("A",9),rep("B",9)))
    tolerance <- 1E-4

    # glmFit <- glm(counts ~ outcome + treatment, data = dobson, contrasts = dobson$type,
    #               family = poisson()) # gold standard

    dataPtrD <- createCyclopsData(Multitype(counts, type) ~ outcome + treatment, data = dobson,
                                       modelType = "pr")

    cyclopsFitD <- fitCyclopsModel(dataPtrD,
                                   prior = createPrior(c("normal","normal"), c(0.0001,10), graph = "type"),
                                   control = createControl(noiseLevel = "silent", maxIterations = 2000))

    cyclopsFitE <- fitCyclopsModel(dataPtrD,
                                   prior = createPrior(c("normal","normal"), c(0.0001,0.0001), graph = "type"),
                                   control = createControl(noiseLevel = "silent"))


})


test_that("Check multitype SCCS", {
#
#   stratumID must be unique by type and all patient entries must be replicated by type
#     (how to specify this in input) ???
#
#     source("helper-conditionalPoisson.R")
#     tolerance <- 1E-6
#     gold.clogit <- clogit(event ~ exgr + agegr + strata(indiv) + offset(loginterval),
#                           data = oxford)
#
#     dataPtr <- createCyclopsData(event ~ exgr + agegr + strata(indiv), time = oxford$interval,
#                                       data = oxford,
#                                       modelType = "sccs")
#     cyclopsFit <- fitCyclopsModel(dataPtr,
#                                   prior = createPrior("none"))
#     expect_equal(logLik(cyclopsFit), logLik(gold.clogit)[1])
#     expect_equal(coef(cyclopsFit), coef(gold.clogit), tolerance = tolerance)
})

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Cyclops documentation built on Aug. 10, 2022, 5:08 p.m.